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Fixed up accuracy layer. #1942

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Fixed up accuracy layer. #1942

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6f1bd27
fixed accuracy layer. With ND Blobs.
BlGene Feb 22, 2015
1440731
Merge PR #2076 into fix_accuracy_layer
BlGene Mar 9, 2015
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7 changes: 7 additions & 0 deletions include/caffe/loss_layers.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -78,7 +78,14 @@ class AccuracyLayer : public Layer<Dtype> {
}
}


int label_axis_, outer_num_, inner_num_;
int top_k_;

/// Whether to ignore instances with a certain label.
bool has_ignore_label_;
/// The label indicating that an instance should be ignored.
int ignore_label_;
};

/**
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71 changes: 47 additions & 24 deletions src/caffe/layers/accuracy_layer.cpp
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Expand Up @@ -14,18 +14,28 @@ template <typename Dtype>
void AccuracyLayer<Dtype>::LayerSetUp(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
top_k_ = this->layer_param_.accuracy_param().top_k();

has_ignore_label_ =
this->layer_param_.accuracy_param().has_ignore_label();
if (has_ignore_label_) {
ignore_label_ = this->layer_param_.accuracy_param().ignore_label();
}
}

template <typename Dtype>
void AccuracyLayer<Dtype>::Reshape(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
CHECK_LE(top_k_, bottom[0]->count() / bottom[1]->count())
<< "top_k must be less than or equal to the number of classes.";
CHECK_GE(bottom[0]->num_axes(), bottom[1]->num_axes());
for (int i = 0; i < bottom[1]->num_axes(); ++i) {
CHECK_LE(bottom[0]->shape(i), bottom[1]->shape(i))
<< "Dimension mismatch between predictions and label.";
}
label_axis_ =
bottom[0]->CanonicalAxisIndex(this->layer_param_.accuracy_param().axis());
outer_num_ = bottom[0]->count(0, label_axis_);
inner_num_ = bottom[0]->count(label_axis_ + 1);
CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
<< "Number of labels must match number of predictions; "
<< "e.g., if label axis == 1 and prediction shape is (N, C, H, W), "
<< "label count (number of labels) must be N*H*W, "
<< "with integer values in {0, 1, ..., C-1}.";
vector<int> top_shape(0); // Accuracy is a scalar; 0 axes.
top[0]->Reshape(top_shape);
}
Expand All @@ -35,32 +45,45 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
Dtype accuracy = 0;
const Dtype* bottom_data = bottom[0]->cpu_data();
const Dtype* bottom_label = bottom[1]->cpu_data();
int num = bottom[0]->count(0, bottom[1]->num_axes());
int dim = bottom[0]->count() / num;
const Dtype* label = bottom[1]->cpu_data();
const int dim = bottom[0]->count() / outer_num_;
const int num_labels = bottom[0]->shape(label_axis_);
vector<Dtype> maxval(top_k_+1);
vector<int> max_id(top_k_+1);
for (int i = 0; i < num; ++i) {
// Top-k accuracy
std::vector<std::pair<Dtype, int> > bottom_data_vector;
for (int j = 0; j < dim; ++j) {
bottom_data_vector.push_back(
std::make_pair(bottom_data[i * dim + j], j));
}
std::partial_sort(
bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
// check if true label is in top k predictions
for (int k = 0; k < top_k_; k++) {
if (bottom_data_vector[k].second == static_cast<int>(bottom_label[i])) {
++accuracy;
break;

unsigned int count = 0;
for (int i = 0; i < outer_num_; ++i) {
for (int j = 0; j < inner_num_; ++j) {
const int label_value = static_cast<int>(label[i * inner_num_ + j]);
if (has_ignore_label_ && label_value == ignore_label_) {
continue;
}
DCHECK_GE(label_value, 0);
DCHECK_LT(label_value, bottom[0]->channels());


// Top-k accuracy
std::vector<std::pair<Dtype, int> > bottom_data_vector;
for (int k = 0; k < num_labels; ++k) {
bottom_data_vector.push_back(std::make_pair(
bottom_data[i * dim + k * inner_num_ + j], k));
}
std::partial_sort(
bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
// check if true label is in top k predictions
for (int k = 0; k < top_k_; k++) {
if (bottom_data_vector[k].second == label_value) {
++accuracy;
break;
}
}
count++;
}
}

// LOG(INFO) << "Accuracy: " << accuracy;
top[0]->mutable_cpu_data()[0] = accuracy / num;
top[0]->mutable_cpu_data()[0] = accuracy / count;
// Accuracy layer should not be used as a loss function.
}

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13 changes: 13 additions & 0 deletions src/caffe/proto/caffe.proto
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Expand Up @@ -333,6 +333,8 @@ message LayerParameter {
optional WindowDataParameter window_data_param = 129;
}



// Message that stores parameters used to apply transformation
// to the data layer's data
message TransformationParameter {
Expand Down Expand Up @@ -367,6 +369,17 @@ message AccuracyParameter {
// the top k scoring classes. By default, only compare to the top scoring
// class (i.e. argmax).
optional uint32 top_k = 1 [default = 1];

// The "label" axis of the prediction blob, whose argmax corresponds to the
// predicted label -- may be negative to index from the end (e.g., -1 for the
// last axis). For example, if axis == 1 and the predictions are
// (N x C x H x W), the label blob is expected to contain N*H*W ground truth
// labels with integer values in {0, 1, ..., C-1}.
optional int32 axis = 2 [default = 1];

// If specified, ignore instances with the given label.
optional int32 ignore_label = 3;

}

// Message that stores parameters used by ArgMaxLayer
Expand Down